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What is Big Data?

Table of Content

Data are obtained as a result of an experiment, as occurs in the physical and biological science; the collection of the data is part and parcel of the experiment itself. The experiment must be designed to produce data from which meaningful results can be derived; and the main problems associated with the collection of data are the problems of the design of the experiment.In the social sciences, and particularly in economics,experimentation is seldom possible, and data must for the most part be collected by requiring people to fill in questionnaires. First, the design of questionnairesis itself a matter raising considerable statistical problems. Secondly, it is often impossible or impracticable to make a survey of all the data in which one is interested. There then arises the question of how a fraction of the field can be surveyed in a way which will provide meaningful results about the whole.

These are the two major problems which occur in the collection of economic or statistical data. Thus if we wish to collect data on peoples’ spending habits, we have to decide not only the sort of information we want, including precise definitions, of the facts we wish to record and hence the sort of questions we must ask people, but also how we shall select particular people who can answer our questions.

Classification of Data

Big data is a term for information sets that are so substantial or complex that conventional information handling applications are deficient. Challenges incorporate examination, catch, information curation, seek, sharing, stockpiling, exchange, representation, questioning, upgrading and data security. The term frequently alludes just to the utilization of prescient investigation or certain other propelled techniques to concentrate esteem from information, and rarely to a specific size of information set. Precision in huge information may prompt more sure basic leadership, and better choices can bring about more prominent operational proficiency, cost decrease and diminished danger.

Assembling the Data

Once collected, data must be assembled into a useful form. This process is the statistical presentation of data. Data are usually presented in tabular form and are frequently made more readily comprehensible by being presented pictorially in charts and graphs. But presentation involves much more than this. The raw data must be classified into forms appropriate to the purpose in hand. Classification cannot proceed in a vacuum and must depend upon theoretical categories which determine whether one or another classification is meaningful.

Thus, if one had a great list of all the economic transactions which took place in an economy over a year; one would have to know the purpose for which the classification was to be used before classifying them. If this purpose were an investigation into the levels of consumption, investment, savings etc, in the company, the classification would have to be designed to take into account the theoretical meaning of these concepts and their relationships.

Summarize the Data

The description of statistical data involves the computation of measures to summarize the data. There are a host of such measures of wide application, of which the commonest perhaps is the average. However, in certain fields highly specialized and complicated measures are required as, for example, when we are concerned with measuring the rate of mortality of a human population. Moreover, it is sometimes impossible to fabricate a measure which will measure precisely what we are after, as, for example, when we want to measure changes in the cost of living.

Economic and social statistics are collected incidentally on intentionally. A considerable body of data is available as a result of administrative acts and in collected only incidentally to them, e.g. statistics of crime, car accidents, numbers of wage and salary earners incidental to payroll tax collections, details of imports incidental to collection of customs duties.Big data can be depicted by the accompanying attributes:

Volume:The amount of produced and put away information. The extent of the information decides the quality and potential understanding and whether it can really be viewed as large information or not.

Assortment:The sort and nature of the information. Group of people successfully utilize the subsequent knowledge.

Speed: In this connection, the velocity at which the information is produced and prepared to meet the requests and difficulties that lies in the way of development and advancement.

Variability:Inconsistency of the information set can hamper procedures to handle and oversee it.

Significance

The significance of enormous information doesn't rotate around the amount of information you have, however what you do with that information. You can collect information from any source and dissect it to discover answers that empower 1) cost diminishments, 2) time decreases, 3) new item advancement and streamlined offerings, and 4) savvy basic leadership. When you consolidate big data with powerful investigation, you can achieve business-related undertakings, for example,

Determining main drivers of disappointments, issues and deserts in close continuous.

Generating coupons at the purpose of offer taking into account the client's purchasing propensities.

Whole hazard portfolios can be recalculated in minutes.

Able to detect false conduct before it influences your association.

Big data can be dissected for bits of knowledge that prompt better choices and vital business moves.